Method and system for mapping attributes of entities

ABSTRACT

This disclosure relates generally to data processing, and more particularly to a system and a method for mapping heterogeneous data sources. For a product being sold globally, there might be one global database listing characteristics of the product, and from various System and method for mapping attributes of entities are disclosed. In an embodiment, the system uses a combination of Supervised Bayesian Model (SBM) and an Unsupervised Textual Similarity (UTS) model for data analysis. A weighted ensemble of the SBM and the UTS is used, wherein the ensemble is weighted based on a confidence measure. The system, by performing data processing, identifies data match between different data sources (a local databases and a corresponding global database) being compared, and based on matching data found, performs mapping between the local databases and the global database.

PRIORITY CLAIM

This U.S. patent application claims priority under 35 U.S.C. § 119 to:India Application No. 201721008321, filed on Sep. 3, 2017. The entirecontents of the aforementioned application are incorporated herein byreference.

TECHNICAL FIELD

This disclosure relates generally to data processing, and moreparticularly to a system and a method for mapping heterogeneous datasources.

BACKGROUND

In various applications, it is required to compare two or more datasources and identify similarity between contents in the data sourcesbeing compared. For example, consider that an organization has multiplebranches spread across the globe. The organization may be maintaining aglobal database that has information pertaining to various products andservices offered and/or managed by the organization. However, it ispossible that when the organization collects data from each of itsbranches, the data is in heterogeneous format, which means each branchmay be using data that is customized as per local standards and/orrequirements that helps each branch effectively manage activities inthat specific locality. That means the organization would end upcollecting data in heterogeneous format.

The inventors here have recognized several technical problems with suchconventional systems, as explained below. If the organization intends tocollect data from different branches and analyze the data, analysisbecomes a hurdle as the data is in heterogeneous format. Existingsystems that facilitate heterogeneous data processing and analysis relyon textual similarity feature based techniques, which are unsupervised.The mechanism used being unsupervised, affects quality of outputs.

SUMMARY

Embodiments of the present disclosure present technological improvementsas solutions to one or more of the above-mentioned technical problemsrecognized by the inventors in conventional systems. For example, in oneembodiment, a processor implemented method for mapping heterogeneousdatabases is provided. Initially, at least one local database as inputis received as input, via one or more hardware processors, by a datamapping system. Further, at least one characteristic and at least onedescription corresponding to each of a plurality of product entries inthe local database are extracted, via the one or more hardwareprocessors, by the data mapping system. Further, by virtue of anautomated mapping, contents of the at least one local database andcorresponding global database are mapped, wherein the automated mappinginvolves the following steps:

A first set of probability distribution and confidence value aregenerated by applying a Supervised Bayesian Model (SBM) on the at leastone characteristic of each of the plurality of product entries in thelocal database and product data from the corresponding global database.Further, a second set of probability distribution and confidence valueare generated by applying an Unsupervised Textual Similarity (UTS) modelon the at least one description of each of the plurality of productentries in the local database and the product data from thecorresponding global database. A weighted confidence score andprobability distribution are generated based on the first set ofprobability distribution and confidence value and the second set ofprobability distribution and confidence value, wherein the weightedconfidence score and probability distribution indicate extent ofsimilarity between the plurality of product entries in the localdatabase and the product data from the corresponding global database.Further, the local database and the global database are mapped based onthe first set of probability distribution and confidence value, thesecond set of probability distribution and confidence value, and theweighted confidence score and probability distribution, via the one ormore hardware processors, by the data mapping system, wherein data fromthe local database are mapped to corresponding data in the globaldatabase.

In another embodiment, a data mapping system is provided. The datamapping system includes at least one hardware processor; and a memorymodule storing a plurality of instructions. The plurality ofinstructions, when executed, cause the hardware processor to receive atleast one local database as input, using an Input/Output (I/O) interfaceof the data mapping system. A mapping module of the data mapping systemthen extracts at least one characteristic and at least one descriptioncorresponding to each of a plurality of product entries in the localdatabase. The mapping module then performs an automated mapping betweenthe local database and a corresponding global database, wherein theautomated mapping further involves the following steps:

A first set of probability distribution and confidence value aregenerated by applying a Supervised Bayesian Model (SBM) on the at leastone characteristic of each of the plurality of product entries in thelocal database and data from corresponding global database. Further, asecond set of probability distribution and confidence value aregenerated by applying an Unsupervised Textual Similarity (UTS) model onthe at least one description of each of the plurality of product entriesin the local database and product data from corresponding globaldatabase. A weighted confidence score and probability distribution aregenerated based on the first set of probability distribution andconfidence value and the second set of probability distribution andconfidence value, wherein the weighted confidence score and probabilitydistribution indicate extent of similarity between the plurality ofproduct entries in the local database and product data in thecorresponding global database. Further, the local database and theglobal database are mapped based on the first set of probabilitydistribution and confidence value, the second set of probabilitydistribution and confidence value, and the weighted confidence score andprobability distribution, via the one or more hardware processors, bythe data mapping system, wherein data from the local database are mappedto corresponding data in the global database.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the invention, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this disclosure, illustrate exemplary embodiments and, togetherwith the description, serve to explain the disclosed principles.

FIG. 1 illustrates an exemplary block diagram of a data mapping systemthat performs the data processing, according to some embodiments of thepresent disclosure.

FIG. 2 is a flow diagram that depicts steps involved in the process ofperforming data processing using the data mapping system, according tosome embodiments of the present disclosure.

DETAILED DESCRIPTION

Exemplary embodiments are described with reference to the accompanyingdrawings. In the figures, the left-most digit(s) of a reference numberidentifies the figure in which the reference number first appears.Wherever convenient, the same reference numbers are used throughout thedrawings to refer to the same or like parts. While examples and featuresof disclosed principles are described herein, modifications,adaptations, and other implementations are possible without departingfrom the spirit and scope of the disclosed embodiments. It is intendedthat the following detailed description be considered as exemplary only,with the true scope and spirit being indicated by the following claims.

FIG. 1 is a block diagram of a data mapping system that performs thedata processing, according to some embodiments of the presentdisclosure. The data mapping system includes an Input/Output (I/O)interface 101, a memory module 102, a mapping module 103, and a hardwareprocessor 104. In an embodiment, the components/terminologies as well asthe number of each component of the data mapping system 100, as depictedin FIG. 1, are for illustration purpose only, and can vary as perimplementation requirements/standards.

The Input/Output (I/O) interface 101 is configured provide at least achannel for facilitating communication between the data mapping system100 and at least one external entity. The external entity can be a userand/or a system. For example, the I/O interface 101 can provide asuitable interface for a user to directly interact with the data mappingsystem 100 so as to provide input and/or to access output and/or toperform one or more action(s) related to the data processing beingcarried out by the data mapping system 100. In another example, the I/Ointerface 101 can be configured to provide at least one channel withsuitable communication protocol(s) to facilitate communicationcomponents of the data mapping system 100. The I/O module 101, byinteracting with the at least one external entity, collects a datasources that are to be processed further, as input. The data sourcesinclude at least one global data source and at least one local datasource that need to be compared and mapped, during the data processing.In an embodiment, the input is of heterogeneous nature. In anembodiment, the data sources (i.e. the global and local data sources) donot share a common key, which means the data in the data sources areheterogeneous.

The memory module 102 is configured to store any type of data associatedwith the data processing being handled by the data mapping system 100.For example, the data sources collected as input for processing arestored in appropriate database(s) in the memory module 102. The data maybe stored permanently and/or temporarily, as configured. The memorymodule 102 further stores result of data processing, which can beprovided as output to a user, instantly and/or as and when required. Thememory module 102 is further configured to provide access to all orselected contents stored, for a user, with or without performing anauthentication check.

The mapping module 103 is configured to collect local and global databases from the memory module 102, and perform data processing toidentify related contents in the databases being analyzed. In anembodiment, the data in the data sources being analyzed are ofheterogeneous nature. For example, the global data source storesstandard terminology and/or definition for a particular entity, whereinthe ‘entity’ being referred to in this context maybe a product and/orprocess. A local data source being analyzed has a different terminologyused for the same entity, along with a definition. The mapping module103, by performing the data processing, identifies relationship betweensimilar data in the local and global data sources, and accordinglyperforms mapping between the related data as identified during the dataprocessing. The mapping module 103, to map the global and localdatabases, can perform an automated mapping and/or a verified mapping.In an embodiment, for any given input, the mapping module 103 performsthe automated mapping by combining a supervised Bayesian model and anunsupervised textual similarity model. Along with mapping resultsobtained using the automated mapping, the mapping module 103 alsogenerates a confidence score for the relation/mapping identified usingthe automated mapping process. The confidence score represents extent ofaccuracy with respect to mapping done between the local and globaldatabases. The confidence score thus generated is further used by themapping module 103 to determine whether user intervention/supervision isrequired in the data mapping process or not. For example, if theconfidence score is less than a pre-defined threshold value, this wouldindicate that the accuracy in mapping done between the global and localdatabases is low, and the mapping module 103 can then allow/prompt auser to intervene and monitor the mapping process (i.e., verifiedmapping). The mapping module 103 can be further configured to performfusion of the databases being mapped, based on one or more matching datafound.

The supervise Bayesian model and the Unsupervised Text Similarity (UTS)being used by the mapping module 103, for the automated mapping, areexplained below (For example purpose, the Bayesian and UTS models areexplained by considering global and local data sources havinginformation related to characteristics such as but not limited toflavor, and brand, of one or more products):

Consider two databases:

-   -   a. Local database (DB(L)) with each product I having local        characteristics L1, L2, . . . LM, and retailer descriptions        (DI), and    -   b. a Global database (DB (G)) having K global characteristics.

Record matching is to be performed where products in local database areto be mapped to global characteristic values (for example, ‘category’,or ‘global brand’ and so on).

From the data-mining point of view, each product I in L has two kind ofinformation (1) ‘M’ Local characteristics and (2) Textual descriptionsby retailers. By using combination of Bayesian model and UTS, value ofglobal characteristic G_(j) is predicted for each product in L. In thedata processing stage,

-   -   (1) Supervised Bayesian Model (SBM) using local characteristics;        and    -   (2) Unsupervised Textual Similarity (UTS) using descriptions

Both the SBM and UTS are used to compute probability of every possiblestate gj,t, t=1, 2, . . . mj of Gj. Separate probability values aregenerated using the SBM and UTS approaches, and further, a weightedensemble based approach is used to combine the probabilities of bothmodels to predict the value of Gj.

Supervised Bayesian Model:

Approach to build SBM comprises of 3 steps:

-   -   (1) Network Structure Learning,    -   (2) Parameter Learning,    -   (3) Bayesian Inference.

A learning Tree based Bayesian Networks (TBN) is used for structurelearning, whereas for parameter learning and Bayesian inference, aprobabilistic query based approach on the databases of conditionalprobability is used.

TBN Structure Learning:

Bayesian networks are associated with parameters known as conditionalprobability tables (CPT), where a CPT of a node indicates theprobability that each value of a node can take given all combinations ofvalues of its parent nodes. In CPTs, the number of bins growsexponentially as the number of parent nodes increases leaving fewer datainstances in each bin for estimating the parameters. Thus, sparserstructures often provide better estimation of the underlyingdistribution. Also, if the number of states of each node becomes highand the learned model is complex, Bayesian inferencing becomesconceptually and computationally intractable. Hence, tree-basedstructures are useful for density estimation from limited data and inthe presence of higher number of states for facilitating fasterinferencing. A greedy search may be used for this purpose, and scorebased approach is used for learning TBN structure.

Given the global characteristic G_(j) and M local characteristics, a setof top η most relevant local characteristics with respect to G_(j) usingmutual information.

These η local characteristics by the set Y^(j) (L). Further, a Treebased Bayesian Network (TBN) on random variables X={X_(r): r=1, 2, . . .η+1} where each X_(r)∈X is either a local characteristic L_(i)∈Y^(j) (L)or global characteristic G_(j).

Cross-entropy between the tree structures distributions and the actualunderlying distribution is minimized when the structure is a maximumweight spanning tree (MST). As a result, in order to learn TBNstructure, MST is learnt for the characteristics in the set X. Mutualinformation between each pair characteristics, denoted by W (Xr;Xs).Further, mutual information is obtained as the weight between each pairof characteristics and learn MST using Kruskal's algorithm.

$\begin{matrix}{{{TotalWeight}({TW})} = {\sum\limits_{{r = 1},{{P_{a}{(X_{r})}} \neq 0}}^{\eta + 1}{W\left( {X_{r},{P_{a}\left( X_{r} \right)}} \right)}}} & (1)\end{matrix}$

By learning MST, order of search space of possible graphs is reduced.Using this MST, the mapping module 103 searches for directed graph withleast cross-entropy, by flipping each edge directions sequentially toobtain 2^(η) directed graphs along with their corresponding TotalWeight(TW) calculated using Equation 1. Graph with maximum TW (minimumcross-entropy) is chosen as the best graphical structure representativeof underlying distribution.

Parameter Learning and Interference

To learn the parameters (CPTs) of the Bayesian Network, for everyproduct I in L probabilities p_(j,1) ^(l), p_(j,2) ^(l), . . . p_(j,m)_(j) ^(l), for every state of G_(j), given the observed values of localcharacteristics in the Bayesian network.

By applying the supervised Bayesian Model on the characteristicsextracted from the contents of the local database, the mapping module103 generates a first set of confidence value and probabilitydistribution.

Unsupervised Text Similarity

UTS approach is used to compute the probability q_(j,1) ^(l), q_(j,2)^(l), . . . q_(j,m) _(j) ^(l) of each possible state of the globalcharacteristic Gj using retailer descriptions. Consider each product Iin L has rl descriptions and for each description d_(l,r), where r=1, 2,. . . rl n-grams of adjacent words are determined. Let N_(l)={n_(v)^(l), v=1,2, . . . } be the set of n-grams of all descriptions, wheref_(v) ^(l) be the frequency of each n_(v) ^(l) defined as a ratio of thenumber of descriptions in which n_(v) ^(l) exists to the r_(l).

For every state g_(j,t) of G_(j), best matching n-gram from the setN_(l) is determined by calculating Jaro-Wrinkler distance betweeng_(j,t) and every n_(v) ^(l) ∈ N_(l) and choose the n-gram, say n_(v,t′)^(l) with the maximum score s_(j,t) ^(l) to get new score s_(j,t)^(l)=s_(j,t) ^(l)*f_(l,t) ^(s). Finally, each score s_(j,t) ^(l) isconverted into the probability q_(j,t) ^(l) by using softmax scalingfunction.

By applying the UTS model on the descriptions extracted from thecontents of the local database, the mapping module 103 generates asecond set of confidence value and probability distribution.

Ensemble of Models

Based on confidence value of both predictions (SBM and UTS), and for agiven probability distribution {P_(j,t) ^(l): t=1, 2, . . . m_(j)} forvalues of G_(j) using SBM model, confidence corresponding to eachprobability is determined as:C(p _(j,t) ^(l))=1−√{square root over (Σ_(t′=1) ^(m) ^(j) (p _(j,t′)^(l) −h _(t′) ^(l)(t))²)}  (2)

where

-   -   t=1, 2, . . . m_(j)    -   h_(t′) ^(l)(t) is ideal distribution, which is 1 when t=t′ and 0        otherwise. Similarly for each probability q_(j,t) ^(l),        confidence C (q_(j,t) ^(l)) is determined.

With the given probability distribution and the confidence values fromboth models, weighted linear sum of two probabilities is taken to getthe new probability distribution over the states of Gj: P_(j,t) ^(l)=C(P_(j,t) ^(l))*P_(j,t) ^(l)+C (q_(j,t) ^(l))*q_(j,t) ^(l), t=1, 2, . . .mj, and value of Gj is chosen for maximum P_(j,t) ^(l). The ensemblereferred to here is a confidence based weighted ensemble i.e. theensemble is weighted based on confidence measure.

Further, the confidence value is compared with a threshold value ofconfidence value, wherein the threshold value of confidence value ispre-defined and configured with the data mapping system 100. If theconfidence value is less than the threshold value, then the data mappingsystem 100 prompts a user to intervene and verify the mapping. If theconfidence score is found to exceed the threshold value, then themapping module 103 continues with the automated mapping process usingthe SBM and the UST models. The mapping module 103 can be furtherconfigured to perform fusion of the data sources based on mappingresults.

The hardware processor 104, (or a plurality of hardware processors) isconfigured to communicate with other components of the data mappingsystem 100, and perform one or more actions/steps as indicated by thecorresponding component, by receiving appropriate data and/or controlsignals from the corresponding component.

FIG. 2 is a flow diagram 200 that depicts steps involved in the processof performing data processing using the data mapping system, inaccordance with some embodiments of the present disclosure. Consider anyproduct ‘A’ that is being sold globally. The same product may be nameddifferently over different parts of the world. Also, descriptionprovided to the same product by different vendors/sellers acrossdifferent parts of the globe also can vary. Assume that there is a‘global database’ which stores data such as but not limited to differentcharacteristics and actual descriptions of at least ‘A’ is stored. Forthe same product, there may be a plurality of ‘local databases’ in whichlocal characteristics and descriptions given for ‘A’ at different partsof the world are stored. As the databases i.e. the global database andthe one or more local databases are heterogeneous (i.e. there is nocommon key between the global and local databases), the data mappingsystem 100 uses the data processing described herein to perform datamapping between the global and local databases.

For explanation purpose, assume that ‘one’ local database is given (202)as input to the data mapping system 100. The data mapping system 100extracts (204) local characteristics of one or more products listed inthe local database. The data mapping system 100 further extracts (206)descriptions of the one or more products listed in the local database.The data mapping system 100 also collects the global database (208)corresponding to the local database as input. The data mapping system100 then applies (210) a Supervised Bayesian Model (SBM) on theextracted local characteristics and data from the global database, andgenerates a first set of probability distribution and a confidencevalue. Similarly, the data mapping system 100 applies (212) anUnsupervised Textual Similarity (UTS) on the descriptions extracted fromthe local database as well as on the data from the global database, andgenerates a second set of probability distribution and a confidencevalue.

The data mapping system 100 then processes the first set of probabilitydistribution and the confidence value and the second set of probabilitydistribution and the confidence value (an ensemble of the first andsecond set) to generate a combined confidence score. The combinedconfidence score represents extent of similarity between the data in thelocal database and that in the global database. The data mapping system100 then compares (216) the combined confidence score with a thresholdscore (referred to as ‘threshold value’). The combined confidence scoreexceeding the threshold value indicates higher accuracy in mapping, andin that case, the data mapping system 100 continues with (218) theautomated mapping as explained in the aforementioned steps. The combinedconfidence score being less than the threshold value indicates that theaccuracy of the automated mapping performed by the data mapping system100 is less, and in that case, the data mapping system 100 performs(220) a verified mapping, wherein a user intervention maybe promptedduring the mapping process. By performing one of the automated mappingand/or the verified mapping, the data mapping system 100 generates (222)mapping results. In an embodiment, after performing mapping between thelocal and global databases, the data mapping system 100 can be used tofuse the global and local databases. In an embodiment, the data mappingsystem 100 takes into consideration, a weighted ensemble of probabilitydistribution from the SBM and UST models, as an input for fusion of thelocal and global databases.

The illustrated steps are set out to explain the exemplary embodimentsshown, and it should be anticipated that ongoing technologicaldevelopment will change the manner in which particular functions areperformed. These examples are presented herein for purposes ofillustration, and not limitation. Further, the boundaries of thefunctional building blocks have been arbitrarily defined herein for theconvenience of the description. Alternative boundaries can be defined solong as the specified functions and relationships thereof areappropriately performed. Alternatives (including equivalents,extensions, variations, deviations, etc., of those described herein)will be apparent to persons skilled in the relevant art(s) based on theteachings contained herein. Such alternatives fall within the scope andspirit of the disclosed embodiments. Also, the words “comprising,”“having,” “containing,” and “including,” and other similar forms areintended to be equivalent in meaning and be open ended in that an itemor items following any one of these words is not meant to be anexhaustive listing of such item or items, or meant to be limited to onlythe listed item or items. It must also be noted that as used herein andin the appended claims, the singular forms “a,” “an,” and “the” includeplural references unless the context clearly dictates otherwise.

Furthermore, one or more computer-readable storage media may be utilizedin implementing embodiments consistent with the present disclosure. Acomputer-readable storage medium refers to any type of physical memoryon which information or data readable by a processor may be stored.Thus, a computer-readable storage medium may store instructions forexecution by one or more processors, including instructions for causingthe processor(s) to perform steps or stages consistent with theembodiments described herein. The term “computer-readable medium” shouldbe understood to include tangible items and exclude carrier waves andtransient signals, i.e., be non-transitory. Examples include randomaccess memory (RAM), read-only memory (ROM), volatile memory,nonvolatile memory, hard drives, CD ROMs, DVDs, flash drives, disks, andany other known physical storage media.

It is intended that the disclosure and examples be considered asexemplary only, with a true scope and spirit of disclosed embodimentsbeing indicated by the following claims.

What is claimed is:
 1. A processor-implemented method for mappingheterogeneous databases, comprising: receiving at least one localdatabase as input, via one or more hardware processors, by a datamapping system; extracting at least one characteristic and at least onedescription corresponding to each of a plurality of product entries inthe local database, via the one or more hardware processors, by the datamapping system; and performing mapping between the local database and acorresponding global database, by performing an automated mapping, bythe data mapping system, wherein the automated mapping comprises of:generating a first set of probability distribution and confidence valueby applying a Supervised Bayesian Model on the at least onecharacteristic of each of the plurality of product entries in the localdatabase and product data from the corresponding global database;generating a second set of probability distribution and confidence valueby applying an Unsupervised Textual Similarity model on the at least onedescription of each of the plurality of product entries in the localdatabase and the product data from the corresponding global database;generating a weighted confidence score and a probability distributionbased on the first set of probability distribution and confidence valueand the second set of probability distribution and confidence value,wherein the weighted confidence score and probability distributionindicate extent of similarity between the plurality of product entriesin the local database and that in the corresponding global database; andmapping the local database and the corresponding global database basedon the first set of probability distribution and confidence value, thesecond set of probability distribution and confidence value, and theweighted confidence score and the probability distribution, wherein allproducts from the local database are mapped to corresponding productdata in the global database.
 2. The method of claim 1, whereingenerating the first set of probability distribution comprises of:selecting a certain number of most relevant local characteristics foreach global characteristic in the global database; generating a maximumWeight Spanning Tree for each global characteristic and correspondinglocal characteristics selected, wherein mutual information between theglobal and the local characteristics is used as edge weight of themaximum Weight Spanning Tree; learning a plurality of ConditionalProbability Tables for data in the maximum Weight Spanning Tree; andcomputing probability value for each state of each global characteristicGj in the global database, with respect to value of the most relevantlocal characteristics selected, as a probability value belonging to thefirst set of probability distribution, based on one or more dependenciesindicated in the plurality of Conditional Probability Tables.
 3. Themethod of claim 1, wherein generating the second probabilitydistribution comprises of: concatenating all retailer descriptionsprovided for each product in the local database; forming n-gram of allthe concatenated descriptions to form a set NI comprising the n-gram ofall the concatenated descriptions; calculating a score that representssimilarity between values from the set NI and global characteristic Gj,for a combination of each element of the set NI and corresponding stateof global characteristic Gj, to generate a set of scores; selecting anelement having maximum score from the set of scores; identifyingfrequency of each element of the set NI, defined as the ratio of numberof descriptions in which element exists; obtaining the calculated scoresfor all states of global characteristic Gj along with correspondingfrequency; and computing probability distribution of each product acrossall the states of global characteristic Gj, as the second probabilitydistribution.
 4. The method of claim 1, wherein performing a verifiedmapping between the local database and the global database, based on theweighted confidence score comprises of: comparing the weightedconfidence score with a threshold score; and selecting and performingthe verified mapping if the generated confidence score is less than thethreshold score.
 5. The method of claim 1, wherein the global databaseand the at least one local database are heterogeneous databases havingno common key.
 6. A data mapping system, comprising: at least onehardware processor; and a memory module storing a plurality ofinstructions, said plurality of instructions, when executed, cause thehardware processor to: receive at least one local database as input,using an Input/Output interface of the data mapping system; extract atleast one characteristic and at least one description corresponding toeach of a plurality of product entries in the local database, using amapping module of the data mapping system; and perform an automatedmapping between the local database and a corresponding global database,by using the mapping module, wherein the automated mapping comprises of:generating a first set of probability distribution and confidence valueby applying a Supervised Bayesian Model on the at least onecharacteristic of each of the plurality of product entries in the localdatabase and product data from the corresponding global database;generating a second set of probability distribution and confidence valueby applying an Unsupervised Textual Similarity model on the at least onedescription of each of the plurality of product entries in the localdatabase and the product data from the corresponding global database;generating a weighted confidence score and a probability distributionbased on the first set of probability distribution and confidence valueand the second set of probability distribution and confidence value,wherein the weighted confidence score and probability distributionindicate extent of similarity between the plurality of product entriesin the local database and that in the global database; and mapping thelocal database and the corresponding global database based on the firstset of probability distribution and confidence value, the second set ofprobability distribution and confidence value, and the weightedconfidence score and the probability distribution, wherein all productsfrom the local database are mapped to corresponding product data in theglobal database.
 7. The data mapping system of claim 6, wherein themapping module-generates the first set of probability distribution by:selecting a certain number of most relevant local characteristics foreach global characteristic in the global database; generating a maximumWeight Spanning Tree for a global characteristic and corresponding localcharacteristics selected, wherein mutual information between the globaland the local characteristic is used as edge weight of the maximumWeight Spanning Tree; learning a plurality of Conditional ProbabilityTables for data in the maximum Weight Spanning Tree; and computingprobability value for each state of each global characteristic Gj in theglobal database, with respect to value of the most relevant localcharacteristics selected, as a probability value belonging to the firstset of probability distribution, based on one or more dependenciesindicated in the plurality of Conditional Probability Tables.
 8. Thedata mapping system of claim 6, wherein the mapping module-generates thesecond set of probability distribution by: concatenating all retailerdescriptions provided for each product in the local database; formingn-gram of all the concatenated descriptions to form a set NI comprisingthe n-gram of all the concatenated descriptions; calculating a scorethat represents similarity between values from the set NI and globalcharacteristic Gj, for a combination of each element of the set NI andcorresponding state of global characteristic Gj, to generate a set ofscores; selecting an element having maximum score from the set ofscores; identifying frequency of each element of the set NI, defined asthe ratio of number of descriptions in which element exists; obtainingthe calculated scores for all states of global characteristic Gj alongwith corresponding frequency; and computing probability distribution ofeach product across all the states of global characteristic Gj, as thesecond probability distribution.
 9. The data mapping system of claim 6,wherein the mapping module performs a verified mapping between the localdatabase and the global database, by: comparing the weighted confidencescore with a threshold score; and selecting and performing the verifiedmapping if the generated confidence score is less than the thresholdscore.
 10. One or more non-transitory machine readable informationstorage mediums comprising one or more instructions which when executedby one or more hardware processors causes: receiving at least one localdatabase as input, via one or more hardware processors; extracting atleast one characteristic and at least one description corresponding toeach of a plurality of product entries in the local database, via theone or more hardware processors; and performing mapping between thelocal database and a corresponding global database, by performing anautomated mapping, wherein the automated mapping comprises of:generating a first set of probability distribution and confidence valueby applying a Supervised Bayesian Model on the at least onecharacteristic of each of the plurality of product entries in the localdatabase and product data from the corresponding global database;generating a second set of probability distribution and confidence valueby applying an Unsupervised Textual Similarity model on the at least onedescription of each of the plurality of product entries in the localdatabase and the product data from the corresponding global database;generating a weighted confidence score and a probability distributionbased on the first set of probability distribution and confidence valueand the second set of probability distribution and confidence value,wherein the weighted confidence score and probability distributionindicate extent of similarity between the plurality of product entriesin the local database and that in the corresponding global database; andmapping the local database and the corresponding global database basedon the first set of probability distribution and confidence value, thesecond set of probability distribution and confidence value, and theweighted confidence score and the probability distribution, wherein allproducts from the local database are mapped to corresponding productdata in the global database.
 11. The one or more non-transitory machinereadable information storage mediums of claim 10, wherein the one ormore instructions which when executed by the one or more hardwareprocessors, for generating the first set of probability distribution,cause: selecting a certain number of most relevant local characteristicsfor each global characteristic in the global database; generating amaximum Weight Spanning Tree for a global characteristic andcorresponding local characteristics selected, wherein mutual informationbetween the global and the local characteristic is used as edge weightof the maximum Weight Spanning Tree; learning a plurality of ConditionalProbability Tables for data in the maximum Weight Spanning Tree; andcomputing probability value for each state of each global characteristicGj in the global database, with respect to value of the most relevantlocal characteristics selected, as a probability value belonging to thefirst set of probability distribution, based on one or more dependenciesindicated in the plurality of Conditional Probability Tables.
 12. Theone or more non-transitory machine readable information storage mediumsof claim 10, wherein the one or more instructions which when executed bythe one or more hardware processors, for generating the second set ofprobability distribution, cause: concatenating all retailer descriptionsprovided for each product in the local database; forming n-gram of allthe concatenated descriptions to form a set NI comprising the n-gram ofall the concatenated descriptions; calculating a score that representssimilarity between values from the set NI and global characteristic Gj,for a combination of each element of the set NI and corresponding stateof global characteristic Gj, to generate a set of scores; selectingelement having maximum score, from the set of scores; identifyingfrequency of each element of the set NI, defined as the ratio of numberof descriptions in which element exists; obtaining the calculated scoresfor all states of global characteristic Gj along with correspondingfrequency; and computing probability distribution of each product acrossall the states of global characteristic Gj, as the second probabilitydistribution.
 13. The one or more non-transitory machine readableinformation storage mediums of claim 10, wherein the one or moreinstructions which when executed by the one or more hardware processors,for performing a verified mapping between the local database and theglobal database, cause: comparing the weighted confidence score with athreshold score; and selecting and performing the verified mapping ifthe generated confidence score is less than the threshold score.